In recent years, computers more often include a user input mode having a touch-sensitive screen on which the user may write with a stylus. This allows the user to input handwritten electronic ink, which is widely considered, for many applications, to be one of the most convenient ways of interacting with a computer. For this mode of user input to be reliable, handwriting recognizers have been developed to interpret the user's handwritten input.
As the technology has matured, many handwriting recognizers now use a neural network that performs an initial analysis and categorization of handwritten input. The use of neural networks has been a major improvement in handwriting recognition; the accuracy of recognition has increased many-fold. To create an accurate neural network, the neural network must be trained—that is, they must be repetitively provided with actual samples of handwritten input and given feedback as to whether the neural network guesses correctly at the interpretation of the handwritten input. Effective training also means that the handwritten input samples are provided from a very large number of different people. This is because everyone has a different style of writing. The neural network should be robust enough to be able to recognize a wide range of writing styles, if user are to be happy with the end product.
Due to the sheer number of handwriting samples that must be obtained, and due to the massive amount of time that must be invested in properly training a neural network, training a neural network from scratch is extremely expensive. Moreover, training is typically performed for only a single language. In other words, a neural network may be particularly trained to recognize writing in the English language, or in the Chinese language. Because there are so many languages that exist in the world, high quality neural networks do not exist for many languages. In fact, for some lesser-known languages, neural networks may not exist at all. There is simply insufficient financial incentive for computer and/or software companies to invest substantial money in building and training neural networks for lesser-known languages.